1. Field of the Invention
This invention relates to a system and apparatus of image recognition, and particularly to that having a simplified structure using active eyes for quick, easy, and accurate image processing.
2. Background of the Art
Picture information is a function of time and space and is characterized by open-ended characteristics (i.e., changing with time), multidimensional data, large scale data, discontinuity such as edges, and noise and uncertainty created during sampling and quantifying processes. Accordingly, an image processing system needs adaptive control processing which follows time change, real time processing which processes a large quantity of data in a real time, non-linear processing which deals with discontinuity, and robust characteristics for noise and fluctuation.
A neural network comprised of arithmetic elements called "neurons" has: mathematical characteristics and information processing capacity, which realize simultaneous parallel processing by using a number of neurons; learning capacity in which weight coupling ratio between neurons change plastically, and optimization capacity for minimizing evaluation equations under complex restraint conditions. By using the above capacities, it is possible to solve problems of large-scale optimization in image recognition, at a high speed. Also, by using the learning capacity, a system, which enables recognition of various images by changing the weight coupling ratio in an adaptive manner, has been suggested.
FIG. 9 shows one example of a conventional image recognition system. In the figure, A is image data to be processed, B is an HSI transformation unit, C is a filter, D is a binarization unit, and E is an outline extracting unit.
As described above, heretofore, image recognition systems use a particular hardware which satisfies requirements for image information processing, resulting in large-scale and costly machinery. On the other hand, without such particular hardware, it takes an extremely long time to recognize images. That is, in the conventional image recognition systems, elimination of normal noises is necessary, and processing of, for example, color transformation must be performed pixel by pixel (picture element by picture element), leading to long processing time for image recognition. Thus, heretofore, in order to shorten time for image recognition, there was no way other than reduction of the number of pixels. However, when reducing the number of pixels, accuracy suffers or is sacrificed.
In addition, the conventional image recognition systems as shown in FIG. 9 cannot provide a result until the last processing step is complete, since each processing is performed in sequence in one direction. Thus, if a processing step ceases for some reason, the interrupted step influences the whole system until the end of the process. Accurate image recognition cannot, therefore, be guarantied.